Token导航 LogoToken导航TokenDH.com
研究检索external-serviceclawhub未标认证来源可访问clear审计通过

ai-tool-researchAI 工具研究

Agent Skill

ai-tool-research 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,766

周安装

113

GitHub Stars

公开资料未说明

下载量

895
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ai-tool-research(AI 工具研究)
来源仓库:https://github.com/nazmul87-wq/ai-tool-research
安装命令:
openclaw skills install ai-tool-research
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install ai-tool-research

简介

AI 工具研究技能分析 Claude Desktop、Cursor 等主流 AI 产品的使用模式。

  • 生成生产力手册与技巧指南,帮助用户优化工作流配置。
  • 适用于新手入门指导或多工具协同场景下的最佳实践推荐。
  • 安装命令为 openclaw skills install ai-tool-research,依赖公开知识库检索。
  • 输出结果仅供参考,实际效果可能因系统版本或个人设置而异。

SKILL.md

name
ai-tool-research
description
>-

AI Tool Research Skill

Generates two Markdown artifacts for a given AI tool, updated for the last N days:

  1. <Tool>-Productivity-Playbook.md — how real people are using it (personas, use cases, unusual examples, links).
  2. <Tool>-Skills-Catalog.md — rated list of skills / extensions / plugins / rules / MCP servers with persona mapping.

The skill is runtime-agnostic. It works in Cursor, Claude Desktop, ChatGPT (web), Codex, Gemini CLI, and OpenClaw. See Usage across runtimes.


When to invoke

Trigger on requests like:

  • "Update the Cursor playbook with what happened this month"
  • "Do the same research for Claude / Codex / Gemini / OpenClaw"
  • "Run the monthly AI tool research"
  • "Rebuild <Tool>-Skills-Catalog.md with fresh ratings"
  • "Do all five tools for this month"

Supported tools

Tool keyWhat it isPrimary sources
claudeClaude Desktop + Claude Code + Anthropic Skillsanthropic.com, anthropics/skills, obra/superpowers, ComposioHQ
cursorCursor AI IDE + Rules / Skills / Pluginscursor.com, cursor.directory, awesome-cursorrules
codexOpenAI Codex (CLI + IDE + App + Cloud)openai.com/codex, openai/skills, openai/codex-plugins
geminiGoogle Gemini app + Gemini CLI + Code Assist + NotebookLM + Gemsai.google.dev, gemini-cli-extensions, Piebald-AI/awesome-gemini-cli-extensions
openclawPeter Steinberger's OpenClaw local AI agentopenclaw.ai, openclaw/clawhub, VoltAgent/awesome-openclaw-skills

All five honor the agentskills.io open SKILL.md standard, so skills from one ecosystem often work in another — this is called out in every catalog.


Inputs

Gather these before starting:

InputRequired?Default
toolyesask the user: one of claude / cursor / codex / gemini / openclaw / all
since_dateno30 days before today (monthly cadence)
output_dirnocurrent working directory
existing_file_modenorewrite (default) or append-appendix (preserves body, adds "Updates since YYYY-MM-DD" appendix)

If tool = all, loop through the five tools and produce ten files total.

Always print today's date and the since_date used at the top of every generated file so the user can verify the time window later.


High-level workflow

Copy this checklist into the conversation and track progress:

Research Progress:
- [ ] 1. Confirm inputs (tool, since_date, output_dir, mode)
- [ ] 2. Check existing files — if present, read them to avoid regressions
- [ ] 3. Research phase — search primary sources with since_date filter
- [ ] 4. Rating phase — apply validity + usefulness rubric to every item
- [ ] 5. Compose Productivity Playbook using playbook-template.md
- [ ] 6. Compose Skills Catalog using catalog-template.md
- [ ] 7. Verify link integrity + date stamps
- [ ] 8. Write files
- [ ] 9. Append run log entry

Do not skip any step. Step 2 is important — if the files already exist, you must read them so your update reflects genuine "what's new" signal, not repeated evergreen content.


Step 1 — Confirm inputs

If a tool isn't specified, ask with a quick multi-choice question. Example:

Which tool should I research this month? 1. Claude Desktop 2. Cursor 3. OpenAI Codex 4. Google Gemini 5. OpenClaw 6. All five

Default since_date = today - 30 days. If the user already ran this skill recently, prefer since_date = last_run_date from the run log (see Step 9).


Step 2 — Check existing files

Check for these files in output_dir:

  • <Tool>-Productivity-Playbook.md
  • <Tool>-Skills-Catalog.md
  • research-log.md (created by Step 9)

If any exist:

  • Read them. Note the existing structure, tone, and skill ratings.
  • Preserve link references that are still valid.
  • In the new file, explicitly list what changed since the last run in a "Changes since <last_run_date>" section near the top.
  • If existing_file_mode = append-appendix, do NOT rewrite the body. Add a new appendix called Updates — <YYYY-MM-DD> at the end with only the deltas.

Step 3 — Research phase

Source categories to cover (in order of trust)

  1. Official — the tool vendor's own docs, blog, changelog, release notes
  2. Official GitHub orgs — e.g., anthropics/, openai/, getcursor/, google-gemini/, openclaw/
  3. Curated "awesome" lists — usually the fastest signal for new skills / rules / plugins
  4. Community marketplaces — ClawHub, cursor.directory, Composio, geminicli.com/extensions
  5. X.com / Twitter — search <tool> since:YYYY-MM-DD for fresh real-user workflows
  6. Dev blogs — dev.to, Medium, Substack, HackerNoon, Pragmatic Engineer, The New Stack
  7. Reddit — r/ClaudeAI, r/cursor, r/ChatGPTCoding, r/Bard
  8. YouTube — transcripts of creator tutorials (last 30 days)

For the exact search-query library per tool, read research-queries.md.

Time filter

Apply date filtering to every search:

  • Google / Web: after:<since_date> (e.g., after:2026-03-21)
  • X.com: since:<since_date>
  • GitHub: filter by pushed:>=<since_date> on repo search; for issues/PRs use updated:>=<since_date>

What to collect per item

For every skill / rule / plugin / use case you plan to include, capture:

  • Canonical name and author/org
  • Canonical URL (prefer the GitHub repo or the vendor's doc page — not a random blog)
  • One-sentence "what it does" description
  • Last-update date (for validity rating)
  • Install count / stars / endorsements if available
  • Persona fit (from personas.md)

Minimum coverage bar

A single-tool run should surface:

  • ≥ 10 new or updated skills / rules / plugins since since_date
  • ≥ 6 new real-world use-case stories (X threads, case studies, blog posts)
  • ≥ 1 officially announced product/feature change (release notes / changelog)
  • ≥ 3 community discussions (Reddit / HN / Discord)

If you can't hit this bar, state so explicitly in the final file and lower the confidence claims.


Step 4 — Rating phase

Apply the rating system from rating-system.md to every skill / rule / plugin entry.

Quick version (details in the reference file):

Validity = is it real + maintained?

  • Verified — Official, OR GitHub push in last ~90 days AND ⭐ > 500 or installs > 1k
  • 🟢 Likely-valid — confirmed repo, moderate traction, recent activity
  • 🟡 Early / Niche — real but new, low traction
  • 🔴 Unverified — mentioned but canonical source couldn't be confirmed

Usefulness = editorial 1–5 stars based on breadth, docs, persona fit, time-to-value.

  • ★★★★★ must-install
  • ★★★★ weekly use
  • ★★★ niche but excellent
  • ★★ narrow fit
  • ★ experimental / novelty

Be honest. If something is hyped but you couldn't confirm maintenance, rate it 🔴 and say so. Do not give sympathy stars.


Step 5 — Compose the Productivity Playbook

Use the exact section structure in playbook-template.md. Do not renumber or rename sections — this is what makes month-over-month diffs useful.

Personas: always cover all eight from personas.md in the same order (PhD/research, solopreneur, marketer, designer, video/creator, developer, PKM/student, sales/finance/ops). If a persona has nothing meaningful for this tool, write one honest paragraph explaining why and move on.

Tone rules:

  • Third person, not "you can…"
  • Concrete verbs, no marketing fluff
  • Every claim cites a link
  • No emojis (except ratings badges)
  • Prefer tables over prose for comparisons

Length: 300–600 lines is the healthy range. Longer than 700 = you're padding.


Step 6 — Compose the Skills Catalog

Use the exact section structure in catalog-template.md.

Mandatory sections (skipping any breaks month-over-month consistency):

  1. Primer — what are the tool's extensibility primitives
  2. Install commands — copy-pasteable
  3. Rating legend (link to or copy from rating-system.md)
  4. Built-in / official foundation skills
  5. Marketplaces / meta-collections
  6. Skills by persona (all eight)
  7. Cross-tool skills (agentskills.io-compatible, works in ≥ 2 ecosystems)
  8. Starter kits (one per persona, copy-paste-able)
  9. How to create your own skill — minimum viable SKILL.md
  10. Safety / vetting protocol
  11. Tier-1 must-installs (5–8 items)
  12. Cross-catalog navigation (links to sibling catalogs)
  13. Master source index

Ratings appendix at the end is encouraged but not required if ratings are already inline.


Step 7 — Verify

Before writing files, run these checks:

  • [ ] Every skill has a name + URL + one-line description + both ratings
  • [ ] Every URL resolves (when a WebFetch-type tool is available, sample-test a handful)
  • [ ] Last updated: line exists near the top of each file
  • [ ] since_date and today_date are both printed
  • [ ] Sibling catalog links use correct relative paths (./Claude-Skills-Catalog.md, etc.)
  • [ ] No invented repo URLs or fake GitHub orgs
  • [ ] Persona order matches personas.md
  • [ ] Rating badges render correctly in Markdown (no broken emojis)

If any check fails, fix before proceeding to Step 8.


Step 8 — Write files

Write to <output_dir>/<Tool>-Productivity-Playbook.md and <output_dir>/<Tool>-Skills-Catalog.md.

File-naming rules:

  • <Tool> capitalization: Claude, Cursor, Codex, Gemini, OpenClaw
  • For Claude specifically, the playbook is historically Claude-Desktop-Productivity-Playbook.md (note the "Desktop"). Preserve that exact name when updating — the skill catalog drops "Desktop" and is just Claude-Skills-Catalog.md.

If existing_file_mode = append-appendix, append to the existing files instead of overwriting.


Step 9 — Append run log

Append a row to <output_dir>/research-log.md (create it if missing):

# AI Tool Research — Run Log

| Date run | Tool | since_date | Mode | New skills found | New use cases | Notable changes |
|---|---|---|---|---|---|---|
| 2026-04-21 | cursor | 2026-03-21 | rewrite | 14 | 9 | Composer 2 GA; 3 new MCP servers |

This is the source of truth for "when did I last run this" on future invocations.


Output contract (what the user sees in chat)

When finished, reply with:

  1. One-line summary: "Generated <Tool>-Productivity-Playbook.md (N lines) and <Tool>-Skills-Catalog.md (M lines), covering <since_date> → <today>."
  2. Top 5 what's-new bullets — what the user should actually care about this month.
  3. Any sources where you hit rate-limits or had to downgrade confidence.
  4. Run-log row that was appended.

Do NOT dump the full file contents into chat. The file exists — let the user open it.


Usage across runtimes

Cursor

  1. Put this whole folder in .cursor/skills/ai-tool-research/ (project-local) or ~/.cursor/skills/ai-tool-research/ (personal).
  2. Ensure Skills are enabled in Cursor settings.
  3. Ask: "Run the AI tool research skill for Cursor this month."

Claude Desktop / Claude Code

  1. Put this folder in ~/.claude/skills/ai-tool-research/ (or the Anthropic Skills path on your OS).
  2. In Claude Desktop, enable the skill under Settings → Skills.
  3. Ask: "Use the ai-tool-research skill to update the Codex playbook."

ChatGPT (web — no file system)

  1. Start a new conversation.
  2. Paste this SKILL.md as the first message, preceded by: You are an agent following this skill definition. Apply it to my next request.
  3. Also paste the five supporting files (playbook-template.md, catalog-template.md, personas.md, rating-system.md, research-queries.md) in subsequent turns — ChatGPT will keep them in context.
  4. Then say: "Run it for Gemini this month."
  5. ChatGPT will return the two Markdown files as assistant messages; copy them into .md files locally.

Gemini CLI

  1. Put this folder in ~/.gemini/skills/ai-tool-research/ (or whichever Skills path your Gemini CLI is configured with — see GEMINI.md).
  2. Ask: "Use ai-tool-research to update the OpenClaw skills catalog."

OpenAI Codex (CLI / IDE / Cloud)

  1. Put this folder in ~/.codex/skills/ai-tool-research/.
  2. Reference in AGENTS.md if you want it auto-loaded per project.
  3. Ask: "Run ai-tool-research for Claude this month."

OpenClaw

  1. Put this folder in ~/.openclaw/workspace/skills/ai-tool-research/.
  2. Restart the gateway if needed.
  3. Message the agent (on WhatsApp / Telegram / Slack): "Run the ai-tool-research skill for all five tools and DM me a summary."

Reference files (read only when needed — progressive disclosure)

  • playbook-template.md — exact section structure + tone guide for the Productivity Playbook
  • catalog-template.md — exact section structure for the Skills Catalog
  • personas.md — the 8 consistent personas, use-case angles, and what "good coverage" looks like per persona
  • rating-system.md — the full validity + usefulness rubric with decision flowcharts
  • research-queries.md — reusable search-query templates per tool, per source type

Read these only when the step requires it — they're progressive disclosure to keep this top-level SKILL.md lean.


Examples of past output

If available, the user's own previous files in output_dir are the best examples:

  • Claude-Desktop-Productivity-Playbook.md + Claude-Skills-Catalog.md
  • Cursor-Productivity-Playbook.md + Cursor-Skills-Catalog.md
  • Codex-Productivity-Playbook.md + Codex-Skills-Catalog.md
  • Gemini-Productivity-Playbook.md + Gemini-Skills-Catalog.md
  • OpenClaw-Productivity-Playbook.md + OpenClaw-Skills-Catalog.md

Match their voice, section numbering, and level of detail. If the user's versions differ from the templates in this skill, prefer the user's version — their file is the source of truth for their stylistic preferences.


Anti-patterns to avoid

  • ❌ Inventing repos or URLs. If you can't confirm it, rate 🔴 or drop it.
  • ❌ Copying the previous month's file and changing the date. Always verify what actually changed.
  • ❌ Giving five stars to everything. The rating system is useless if everything is ★★★★★.
  • ❌ Marketing-speak ("revolutionary", "game-changing", "unlock"). Use concrete verbs.
  • ❌ Long prose where a table would communicate faster.
  • ❌ Skipping persona sections because "nothing new this month" — write one honest paragraph instead.
  • ❌ Mentioning the skill file internals to the user. They just want the output files.

*This skill is intentionally portable — no hard-coded paths, no runtime-specific features. It works because Claude, Cursor, Codex, Gemini, and OpenClaw all honor the agentskills.io open spec.*

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

92.13%
按下载量换算825

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills